Grad Classes in Course 14 (Part 1)

Thinking about grad school? Consider taking grad classes before applying. Trust me, you won’t regret it. For one, you get a flavor of what a Ph.D. program requires of its students (math, math, math). Second, you get to view economics from a different, more formal perspective (though not entirely removed from a 14.04 or 14.32 point of view). Conditional on you succeeding in such a course, you also get to signal to the Ph.D. admissions committees that you’re tough enough to complete their program.

This past Spring, I took 14.382 and 14.387 with Professors Hausman and Angrist, respectively. By mid-semester, I was calculating asymptotic distributions in my sleep and had whole sections of Mostly Harmless Econometrics memorized. The problem sets were brutal and holistically tried my econometrics skills, from Stata and Matlab programming to probabilistic convergence theorems. After this experience, theoretical econometrics no longer features in my list of future research goals. Instead, I feel that I’ve gained an arsenal of tools to better approach applied economics fields. For example, I’m thinking of taking 14.771, a course where many of the techniques I learned this semester (e.g. diff-in-diff, Wald estimators, clustering) will come up again.

In my next post, I’ll be a bit more specific about the grad classes here at MIT and distinguish between first-year and second-year classes.